Principal Component Analysis in Cowpea for Different Genotypes under Field Condition

Abstract

Principal Component Analysis (PCA) is a multivariate statistical technique widely used in plant breeding to analyse and simplify complex datasets involving multiple traits. It converts correlated variables into a smaller set of uncorrelated variables called principal components, each representing a portion of the total variability present in the data. In practical crop improvement, PCA plays a crucial role in identifying key traits that contribute most to genetic diversity among genotypes A field experiment was conducted at College of Agriculture (Sri Karan Narendra Agriculture University, Jobner) Fatehpur-Shekhawati, Sikar of Eighteen diverse cowpea genotypes were evaluated at the experimental farm during the kharif season 2024-25. The experiment was conducted in Randomized Block Design (RBD) with three replications and each genotype was sown in 4 m length with four rows per plot. Rows were spaced 30 cm apart and plants within rows at 10 cm. ten quantitative traits were analyzed using Principal Component Analysis (PCA) based on standardized data. Out of ten principal components, four components (PC1–PC4) with eigenvalues >1 explained 74.75% of total variation, with PC1 (28.36%) contributing the highest variability. PC1 was associated with growth and maturity traits, while PC2 represented yield-related traits such as grain yield, seed weight, and plant height. PCA revealed strong positive associations among yield traits and negative relationships with maturity traits, indicating trade-offs. The results suggest that traits like grain yield per plant, 100-seed weight, and plant height can be used as key selection criteria. The trait combinations for selection and cross combinations in cowpea breeding facilitating the identification of promising parental lines and accelerating genetic improvement for yield and adaptation.